Prompt tracking of indoor airborne contaminant source location with probability-based inverse multi-zone modeling

Indoor air quality (IAQ) has a significant influence on occupants' comfort, health, productivity, and safety. Existing studies show that the primary causes of many IAQ problems are various airborne contaminants that either are generated indoors or penetrate into indoor environments with passive or active airflows. Accurate and prompt identification of contaminant sources can help determinate appropriate IAQ control solutions, such as, eliminating contaminant sources, isolating and cleaning contaminated spaces. This study develops a fast and effective inverse modeling method for identifying indoor contaminant source characteristics. The paper describes the principles of the probability-based adjoint inverse modeling method and formulates a multi-zone model based inverse prediction algorithm that can rapidly track contaminant source location with known source release time in a building with many compartments. The paper details the inverse modeling procedure with modification of an existing multi-zone airflow and contaminant transport simulation program. The application of the method has been demonstrated with two case studies: contaminant releases in a multi-compartment residential house and in a complex institutional building. The numerical experiments tested the source identification capability of the program for various contaminant sensing scenarios. The investigation verifies the effectiveness and accuracy of the developed method for indoor contaminant source tracking, which will be further explored to identify more complicated indoor contamination episodes.     

Location identification for indoor instantaneous point contaminant source by probability-based inverse Computational Fluid Dynamics modeling

Indoor pollutions jeopardize human health and welfare and may even cause serious morbidity and mortality under extreme conditions. To effectively control and improve indoor environment quality requires immediate interpretation of pollutant sensor readings and accurate identification of indoor pollution history and source characteristics (e.g. source location and release time). This procedure is complicated by non-uniform and dynamic contaminant indoor dispersion behaviors as well as diverse sensor network distributions. This paper introduces a probability concept based inverse modeling method that is able to identify the source location for an instantaneous point source placed in an enclosed environment with known source release time. The study presents the mathematical models that address three different sensing scenarios: sensors without concentration readings, sensors with spatial concentration readings, and sensors with temporal concentration readings. The paper demonstrates the inverse modeling method and algorithm with two case studies: air pollution in an office space and in an aircraft cabin. The predictions were successfully verified against the forward simulation settings, indicating good capability of the method in finding indoor pollutant sources. The research lays a solid ground for further study of the method for more complicated indoor contamination problems. PRACTICAL IMPLICATIONS: The method developed can help track indoor contaminant source location with limited sensor outputs. This will ensure an effective and prompt execution of building control strategies and thus achieve a healthy and safe indoor environment. The method can also assist the design of optimal sensor networks.     

Protecting a whole building from critical indoor contamination with optimal sensor network design and source identification methods

To maintain a healthful and secure indoor environment, it is crucial to design an effective indoor air quality (IAQ) sensor network and interpret sensor outputs for prompt IAQ controls. This paper introduces how a probability concept based inverse modeling method – the adjoint probability method – can be used to assist in designing a high-performance IAQ sensor network and identifying potential contaminant source locations for an entire building based on limited sensor outputs. The study proposes a new IAQ sensor network design and optimization method for buildings with one or more compartments on the basis of the probability calculation. With responses from optimized sensors, a two-stage integrated inverse prediction algorithm is developed that is able to identify a potential IAQ source zone (or room) in a building as well as an exact location within the room. The paper demonstrates the design of sensor networks and the application of the source identification algorithms for a residential dwelling. The case study verifies the feasibility, effectiveness and accuracy of the proposed sensor design method and the two-stage algorithm for indoor contaminant control.     

Inverse identification of the release location,temporal rates,and sensor alarming time of an airborne pollutant source

With an accidental release of an airborne pollutant, it is always critical to know where, when, and how the pollutant has been released. Then, emergency measures can be scientifically advised to prevent any possible harm. This investigation proposes an inverse model to identify the release location, the temporal rate profile, and the sensor alarming time from the start of a pollutant release. The first step is to implement the inverse operation to the cause–effect matrix to obtain the release rate profiles for discrete candidate scenarios with concentration information provided by one sensor. The second step is to interpret the occurrence probability of each solution in the first step with the Bayesian model by matching the concentration at the other sensor. The proposed model was applied to identify a single pollutant source in a two‐dimensional enclosure using measurement data and in a three‐dimensional aircraft cabin with simulated data. The results show that the model is able to correctly determine the pollutant source location, the temporal rate profile, and the sensor alarming time. The known conditions for input into the inverse model include a steady flow field and the valid temporal concentrations at two different locations.     

The Danish Twin Apartment Study – Part II: Mathematical modeling of the relative strength of sources of indoor air pollution

Abstract This study deals with the modeling of air pollution in apartments from laboratory measurements of source strengths, using formaldehyde and Total Volatile Organic Compounds (TVOCs) as model pollutants. The sources in two test apartments were grouped into two: building-related sources and occupant-related sources. The measured source strengths and ventilation rates were used for the prediction of concentrations expected in the apartments. These predictions were compared to measurements in the apartment over 12 months. The conclusions were that the model predictions based on emission rates measured in the laboratory can be used to predict the long-term concentration of the two model pollutants in the apartments. Considering the measured differences in ventilation between the apartments, an occupant emission rate of between 0.2 and 0.3 mg/h/kg body weight could be estimated. Based on previous suggested limits of acceptable exposures of humans to VOCs, an acceptable average emission rate of VOCs from building materials in general was estimated to be about 30 (μ/m²/h. The modeling showed that during the first 200 days, building materials dominated the emissions. After this, sources relating to the occupants dominated. On average about half of the VOC pollution originated from the building materials.     

Removal of contaminants released from room surfaces by displacement and mixing ventilation: modeling and validation

This paper presents the experimental and numerical modeling of contaminant dispersion in a full-scale environmental chamber with different room air distribution systems. For the experimental modeling, an area source with uniform emissions of a hypothetical contaminant (SF6) from the entire floor surface is designed and constructed. Two different types of ventilation are studied: displacement and mixing ventilation. A computer model for predicting the contaminant dispersion in indoor spaces was validated with experimental data. The validated model is used to study the effects of airflow and the area-source location on contaminant dispersion. Results show that the global airflow pattern has a strong impact on the distribution of the contaminants. In general, the personal exposure could be estimated by analyzing the relative source positions in the airflow pattern. Accordingly, the location of an exhaust diffuser may not greatly affect the airflow pattern, but can significantly affect the exposure level in the room. PRACTICAL IMPLICATIONS: When designing ventilation in addition to bringing fresh air to occupants, it is important to consider the removal of contaminants released in the off-gassing of building materials. Typical indoor off-gassing examples are emissions of volatile organic compounds from building enclosure surfaces such as flooring and painted walls. In this study, we conducted experimental and numerical modeling of different area sources in a mock-up office setup, with displacement or mixing ventilation. Displacement ventilation was as successful as mixing ventilation in removing the contaminant source from the floor area. Actually, the most important consideration in the removal of these contaminants is the relative position of the area source to the main airflow pattern and the occupied zone.     

Simulating indoor concentrations of NO(2) and PM(2.5) in multifamily housing for use in health-based intervention modeling

Residents of low-income multifamily housing can have elevated exposures to multiple environmental pollutants known to influence asthma. Simulation models can characterize the health implications of changing indoor concentrations, but quantifying the influence of interventions on concentrations is challenging given complex airflow and source characteristics. In this study, we simulated concentrations in a prototype multifamily building using CONTAM, a multizone airflow and contaminant transport program. Contaminants modeled included PM(2.5) and NO(2) , and parameters included stove use, presence and operability of exhaust fans, smoking, unit level, and building leakiness. We developed regression models to explain variability in CONTAM outputs for individual sources, in a manner that could be utilized in simulation modeling of health outcomes. To evaluate our models, we generated a database of 1000 simulated households with characteristics consistent with Boston public housing developments and residents and compared the predicted levels of NO(2) and PM(2.5) and their correlates with the literature. Our analyses demonstrated that CONTAM outputs could be readily explained by available parameters (R(2) between 0.89 and 0.98 across models), but that one-compartment box models would mischaracterize concentrations and source contributions. Our study quantifies the key drivers for indoor concentrations in multifamily housing and helps to identify opportunities for interventions. PRACTICAL IMPLICATIONS: Many low-income urban asthmatics live in multifamily housing that may be amenable to ventilation-related interventions such as weatherization or air sealing, wall and ceiling hole repairs, and exhaust fan installation or repair, but such interventions must be designed carefully given their cost and their offsetting effects on energy savings as well as indoor and outdoor pollutants. We developed models to take into account the complex behavior of airflow patterns in multifamily buildings, which can be used to identify and evaluate environmental and non-environmental interventions targeting indoor air pollutants which can trigger asthma exacerbations.     

Examining the functional range of commercially available low-cost airborne particle sensors and consequences for monitoring of indoor air quality in residences

Low-cost airborne particle sensors are gaining attention for monitoring human exposure to indoor particulate matter. This study aimed to establish the concentrations at which these commercially available sensors can be expected to report accurate concentrations. We exposed five types of commercial integrated devices and three types of “bare” low-cost particle sensors to a range of concentrations generated by three different sources. We propose definitions of upper and lower bounds of functional range based on the relationship between a given sensor's output and that of a reference instrument during a laboratory experiment. Experiments show that the lower bound can range from approximately 3 to 15 μg/m³. At greater concentrations, sensor output deviates from linearity at approximately 300-3000 μg/m³. We also conducted a simulation campaign to analyze the effect of this limitation on functional range on the accuracy of exposure readings given by these devices. We estimate that the upper bound results in minimal inaccuracy in exposure quantification, and the lower bound can result in as much as a 50% error in approximately 10% of US homes.     

Passive control methods of Kerala traditional architecture for a comfortable indoor environment: A comparative investigation during winter and summer

Kerala is a strip of land on the southwest coast of India lying between Arabian Sea on the west and Western Ghats on the east. The traditional architecture of Kerala is known for its use of natural and passive methods for a comfortable indoor environment. However, it has not been proved by a detailed and quantitative evaluation method so far. A field study was thus conducted in the winter and summer periods to investigate the indoor environmental condition of a typical Kerala traditional residential building. The objective of the investigation was to understand the passive environment control system of Kerala traditional architecture by quantitative analysis of various thermal comfort parameters. It was done by continuously monitoring the indoor and outdoor conditions using a custom made instrument called “Architectural Evaluation System”. The results show that the natural and passive control system of Kerala traditional architecture provides comfortable indoor environment irrespective of the outdoor climatic conditions.     

Dampness in buildings as a risk factor for health effects, EUROEXPO: a multidisciplinary review of the literature (1998-2000) on dampness and mite exposure in buildings and health effects

The scientific literature on health effects from dampness in buildings, including mite exposure over the period 1998-2000 has been reviewed by an European group (EUROEXPO) of eight scientists in experience from medicine, epidemiology, toxicology and engineering. Forty studies deemed relevant have been the foundation for the conclusions. Dampness in buildings is a risk factor for health effects among atopics and non-atopics both in domestic and in public environments. However, the literature is not conclusive in respect of causative agents, e.g. mites, microbiological agents and organic chemicals from degraded building materials. There is a strong need for more multidisciplinary studies including expertise from all relevant areas. A general conclusion from the work was that there is a strong need for multidisciplinary reviews in scientific journals of articles dealing with associations between indoor environmental factors and health effects. PRACTICAL IMPLICATIONS: There is good evidence for a true association between dampness in buildings and health. As the causative factors behind this association are not known, the main focus in practical investigations should be on finding out and remediate the reasons for the humidity problem.